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\title{Multi Agent Systems: scenario description and experimentation strategy}
\author{Roderick Smets and Jef Hermans}

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\maketitle


\section{Scenario Description}
We have chosen to solve the following scenario:

\begin{itemize}
 \item Discrete world, consisting of roads (both one way or two way) and crossroads.
 \item Delivery trucks that can only fit one package at a time.
%  \item Packages are deployed on random crossroads at random times with random delivery points.
 \item Packages are deployed on random crossroads with random delivery points with a fixed time pattern.
 \item We want to optimize the total time from package deployment to package delivery.
 \item Random roadblocks or traffic jams can happen at any time at any point, and the main focus of our solution is preventing unnecessary delays by accounting for these changes.
 
\end{itemize}

\section{Experimentation Strategy}
\subsection{Metrics}
We want to minimize the total time from package deployment to package delivery. Hence we measure the average time it takes to deliver a package that was spawn at a random time. 

\subsection{Scenarios}
For all our scenarios we use the map of Leuven as a discrete world, as we did in the PC lab exercise sessions. This is a rather extended and advanced network.

The following parameters of the coordination mechanism may affect the performance, flexibility and scalability:
 \begin{itemize}
  \item evaporation rate of the intentions committed at the pickup points
  \item evaporation rate of the pheromones in the environment
  \item pace at which the different types of ants propagate (information) in the environment
  \item number of ants sent out
  \item range of feasibility ants
 \end{itemize}


\subsubsection{Scenario 1}
The first scenario is a normal scenario in the sense that there are no dynamics with respect to the environment, the map topology is not changed during simulation and there are no changes in road characteristics.
At time 0 the world is provided with a fixed amount (\textbf{10}) of delivery trucks, placed at random positions. During simulation, a fixed amount (\textbf{5}) of packages pops up with a certain time pattern (\textbf{long interval}). 

\subsubsection{Scenario 2}
The second scenario is a variant of the first one, but the parameters are changed.
The amount of delivery trucks is the same, but the amount (\textbf{10}) of packages deployed with a fixed time pattern is increased. 

These results should be slightly worse than those of scenario 1. This is the case because there are less trucks available per package, so the probability that a package is delivered faster is higher, if a significant amount of experiment runs is considered.

\subsubsection{Scenario 3}
The third scenario is a variant of the first one, but the parameters are changed.
The amount of delivery trucks is the same, but the frequency at which the packages are deployed in the environment is increased (short interval). 

With this experiment we can check the flexibility of the delivery trucks. This experiment should yield worse results in comparison with the results of scenario 1 since the delivery truck agents cannot cope with the high pace at which the packages are deployed in the environment. Hence some packages will have to wait longer till pickup and this will increase the average time from package deployment to delivery.

\subsubsection{Scenario 4}
The fourth scenario is a variant of the first one, but the impact on dynamics in the environment is tested. At random times there are road blocks which inhibit the use of these roads during a certain interval.

This scenario is a check for flexibility. The road blocks will affect the overall performance, but this should be minimal. This scenario has to show that a delivery truck agent reconsiders its intention at a good balanced pace. Hence the performance of this scenario also depends on the rate at which the ants come up with new beliefs. 
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